Reasoning effort is not a "higher is more professional" switch. On flagship models like GPT-5.6 Sol, stronger reasoning can improve complex tasks — but it also adds latency and token usage. The right move is to tier effort by task, not crank every request to the maximum.
1What Is Reasoning Effort?
In the Responses API, reasoning.effort tells the model how much reasoning compute to spend on a single request. Supported values vary by model and may include none, minimal, low, medium, high, and xhigh. Preview coverage for Sol has also mentioned higher tiers — always confirm the exact values on OpenAI's model page and in your console.
Lowering effort typically yields faster responses and fewer reasoning tokens. Raising it helps on harder problems, but it does not redefine the model's overall capability ceiling. Think of effort as a per-request depth knob, not a permanent upgrade.
2Choosing Effort by Task Tier
| Effort | Typical Tasks | Key Point |
|---|---|---|
| Low | Rewrites, classification, format conversion, simple Q&A | Low cost of failure |
| MediumDefault | Enterprise assistants, code assist, moderate analysis | Most production starting point |
| High | Complex debugging, long-horizon planning, security review | Quality over latency |
| Higher | Async deep research, long-running agents | Only when benchmarks prove ROI |
Higher effort usually means higher latency and more reasoning tokens in tandem. Real-time chat, batch pipelines, and low-risk automation should not default to the top tier.
3How Effort Affects Cost and Latency
Reasoning tokens are billed separately from output tokens on many reasoning models. A high-effort call that looks fine in a demo can quietly multiply monthly spend once traffic scales. Latency follows the same curve — P99 response time often jumps before average latency does, which is painful in user-facing flows.
When latency budgets are tight (chat UIs, synchronous tool calls), prefer lower tiers and reserve high effort for async jobs where users expect a wait. Log usage on every call and track effort level alongside token counts and response time.
4When Not to Use High Effort
- Simple, repeatable tasks — paraphrasing, tagging, or schema mapping rarely benefit from deep reasoning.
- Low failure cost — if a wrong answer is cheap to fix or retry, extra reasoning is wasted spend.
- Latency-sensitive paths — live chat and interactive copilots need sub-second feel; max effort works against that.
- Uniform global defaults — one "always high" setting ignores that different endpoints carry different risk profiles.
5Let Benchmarks Set Your Default
Recommended strategy: start at medium, run an evaluation set that mirrors production, and compare quality, latency, and token usage before moving up or down. Route effort by task type, record usage metadata, and watch P99 latency — not just averages.
- Task routing — map effort to endpoint or job type in your API gateway, not in scattered client code.
- Usage logging — tag every call with effort level, reasoning tokens, and wall-clock time for cost attribution.
- Latency monitoring — alert on P99 spikes when you raise effort; quality gains should justify the wait.
6Common Questions
Which values does Sol support?
It depends on the model and preview stage. Use the supported values listed on OpenAI's model page — do not assume parity with GPT-5.2 or other tiers.
Should I set everything to max effort?
No. Simple tasks see little quality gain while latency and cost climb. Route effort by task risk and complexity instead.
How is effort different from ultra mode?
Effort controls reasoning depth per request. Ultra mode is a sub-agent orchestration pattern for longer-horizon work — different layer, different use case.
7Run Your Sol Integration Workflow on Mac mini
When wiring up Sol API calls, macOS gives you Python, Node, and Docker without WSL headaches. A Mac mini M4's unified memory handles eval suites and IDE workloads comfortably, ~4W idle draw suits always-on regression runs, and Gatekeeper plus SIP add a sensible isolation layer for API keys. If you are building routing and benchmark infrastructure, Mac mini M4 is one of the most cost-effective local nodes available — a solid place to start today.
Key Takeaways
Use lower or medium effort for routine and low-risk work. Raise it for complex code, long-running agents, and security review. Reserve the highest tiers only when benchmarks show the benefit outweighs the cost.
- 1Default to medium and establish an eval baseline
- 2Route effort by task risk and complexity
- 3Log usage and keep a fallback model during preview
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